Financial services
Your AI programme isn't
short of models.
It's short of a place to put them. OneDroid Synapse is one governed integration point for every tool and data source your agents touch; OneDroid Engram is the context that survives when the model changes. Built for AI teams at brokers, asset managers, FCMs and capital-markets firms — and built to clear the security, compliance and operations gates on the way through.
For the AI team
When a frontier model is just an
API call away, context is the moat.
Every firm on the street buys the same models, on the same terms, with the same API key. The model is a brain in a jar — swappable, and getting cheaper. What it knows about your business is not, and that is the only part a competitor cannot buy.
That knowledge is context: access to your data, access to your tools, and memory that persists. What separates a team that ships from one that demos is the layer underneath it — how many times you rebuild the same integration, how much context you lose between tools, and how long each new use case waits in review. That is the part OneDroid replaces.
One integration, not one per agent
Connect your tools, systems and data to OneDroid Synapse once. Every agent your team builds inherits the whole surface with a token — no new connector work, no new credential handling, no new review for each one.
Context that outlives the model
OneDroid Engram holds the firm's durable context — decisions, documents, domain knowledge, prior work — versioned and permissioned. Agents read and write the same store, so knowledge compounds instead of resetting.
Change the model without a migration
The harness and the model are the swappable end. Move from one client or provider to another, run two side by side, or test an open-source model — the tools-and-context layer doesn't move.
Evidence as a by-product
Every tool call is authenticated, policy-checked and written to an audit log you own. You don't assemble the compliance story afterwards; running the platform produces it.
Then it clears the gates
Most AI programmes don't fail on capability.
They stall in review.
In a regulated firm the blocker is rarely "can the model do it." It is the security review, the supervision question and the operational one — and they arrive after the work is built, which is why they hurt. Because OneDroid Synapse sits in the call path from day one, the answers exist before anyone asks.
Security
One place where "what can AI reach here?" is decided and recorded. Credentials held per user, per hub. Sensitive data detected and redacted in the request path rather than in a batch job afterwards.
Supervision and records
Retention and supervisory obligations assume a record exists. AI use on personal accounts produces none at all. This produces a tamper-evident one a third party can check.
Operations
Deployed in your own cloud, on your own Postgres. Export is a SQL query. The same mechanism whether it's one team or the whole firm.
The order matters. Governance sold as the point is a tax nobody volunteers for. Governance that arrives as a property of a platform your AI team actually wants is how the programme gets unblocked — and how the security and compliance functions get an answer they can act on.
Build or buy
An MCP proxy is a weekend.
The one that passes review isn't.
Your architects could wire agents to your systems themselves, and they know it. We'd rather say that plainly than pretend the integration is hard. The integration isn't what takes two quarters — the review does. Here is what a security reviewer asks, and what each answer costs to build.
"Whose credentials is the agent using?"
Credentials bound per user, per workspace, per account — encrypted at rest with AES-256-GCM, individually revocable, never sitting in a connection string. Not one shared service account carrying everyone's access.
"What can this agent reach, and who decided?"
Tool-level allow and deny policy, with the decision recorded at the moment it was made — not reconstructed from logs afterwards, which is not the same thing and a reviewer knows it.
"How do I know the log wasn't edited?"
The expensive one. A hash chain, a Merkle tree, canonical serialisation, a transactional outbox so writes can't be lost, a reconciler, a circuit breaker — and an independent verifier, because a system attesting to itself proves nothing.
"What stops client data reaching the model?"
Detection in the request path, returning a redaction reference for audit — not a nightly batch job that finds it after the fact. And tuned rather than naive: ours excludes date patterns from free-text lanes, because specification documents are full of dates.
"Which third parties see this?"
A subprocessor list you are willing to publish, with an answer for embeddings. Ours is on the privacy page, named rather than buried.
"What happens when we leave?"
Your data is in your own Postgres. Export is a SQL query. There is nothing to extract from us because we were never holding it.
Any one of those is a sprint. All of them, tested, and defensible in front of an examiner, is two quarters of your platform team not building product. That is the trade, stated honestly — not "you couldn't build this," but "you probably shouldn't."
OneDroid Engram
Portable, persistent context —
for every AI application you run.
Models are interchangeable. Your firm's context is not. OneDroid Engram is where that context lives so it can be reused by everything: the coding agents, the research assistants, the internal chat tool, the thing a team builds next quarter.
Portable
Reachable by any MCP client, from any harness, behind any model. Context written by one agent is available to the next one — including the one you haven't chosen yet.
Persistent and versioned
Objects carry revision history, so you can see what an agent knew and when it changed. Knowledge accumulates as an asset instead of evaporating at the end of a session.
Rich, not just a vector blob
Hybrid retrieval — semantic and keyword together — over a typed object graph, so relationships like "supersedes" or "references" are explicit and traversable rather than guessed from similarity.
Yours, and permissioned
Point it at a Postgres you own and documents, embeddings, history and permissions are rows in your database. Access is scoped by group and namespace, so context is shared deliberately.
This is the part that compounds. An audit trail is worth more the longer it runs, and so is a context store — a firm starting today cannot buy back the history yours has been accumulating.
Evidence
Proven where the audits are unforgiving.
The flagship deployment is a US enterprise handling regulated clinical data — a different industry with the same shape: sensitive data, external auditors, and customers who run their own security reviews. In two quarters it went from "can we even use AI here" to a running, audited platform.
129
enterprise tools brought under one governed gateway, serving live traffic.
16 scenarios
in the published QA record — six pass, three fail, five conditional. The failures are published, which is why the passes carry weight.
0
AI-generated compliance errors shipped. One was produced, caught by a ground-truth cross-check, and stopped before release.
That last number is the one worth reading twice. The universal fear about AI in a regulated workflow is that it confidently invents something and a human ships it. Here that happened — and the process caught it, recorded it, and stopped it.
Stated plainly
What we don't claim.
Not "nothing leaves"
Model inference is an outbound call — for us and for everyone using commercial AI. Your data comes to rest inside your boundary; the inference call that transits is gated, scanned and logged. Anyone promising otherwise hasn't read their own architecture.
Not a safety guarantee
OneDroid Synapse transports, authorises and logs tool calls. It does not vouch for an agent's judgement, and no product does. We sell visibility and accountability, not guarantees.
Not a financial-services logo yet
The flagship reference is in regulated healthcare. Being early is the trade, and design partners get roadmap influence for it. We publish what we can prove and nothing else.
Start with one use case.
The fastest way in is a single workflow your AI team already wants to ship — governed end to end, with the evidence trail from the first call.
michal@onedroid.ai